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reviews.bib
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@ARTICLE{Dopp2022-di,
title = "Data-driven science and machine learning methods in laser-plasma
physics",
author = "D{\"o}pp, Andreas and Eberle, Christoph and Howard, Sunny and
Irshad, Faran and Lin, Jinpu and Streeter, Matthew",
abstract = "Laser-plasma physics has developed rapidly over the past few
decades as lasers have become both more powerful and more widely
available. Early experimental and numerical research in this
field was dominated by single-shot experiments with limited
parameter exploration. However, recent technological
improvements make it possible to gather data for hundreds or
thousands of different settings in both experiments and
simulations. This has sparked interest in using advanced
techniques from mathematics, statistics and computer science to
deal with, and benefit from, big data. At the same time,
sophisticated modeling techniques also provide new ways for
researchers to deal effectively with situation where still only
sparse data are available. This paper aims to present an
overview of relevant machine learning methods with focus on
applicability to laser-plasma physics and its important
sub-fields of laser-plasma acceleration and inertial confinement
fusion.",
publisher = "arXiv",
year = 2022
}
@ARTICLE{Anirudh2022-rv,
title = "2022 review of data-driven plasma science",
author = "Anirudh, Rushil and Archibald, Rick and Asif, M Salman and
Becker, Markus M and Benkadda, Sadruddin and Bremer, Peer-Timo
and Bud{\'e}, Rick H S and Chang, C S and Chen, Lei and
Churchill, R M and Citrin, Jonathan and Gaffney, Jim A and
Gainaru, Ana and Gekelman, Walter and Gibbs, Tom and Hamaguchi,
Satoshi and Hill, Christian and Humbird, Kelli and Jalas,
S{\"o}ren and Kawaguchi, Satoru and Kim, Gon-Ho and Kirchen,
Manuel and Klasky, Scott and Kline, John L and Krushelnick, Karl
and Kustowski, Bogdan and Lapenta, Giovanni and Li, Wenting and
Ma, Tammy and Mason, Nigel J and Mesbah, Ali and Michoski, Craig
and Munson, Todd and Murakami, Izumi and Najm, Habib N and
Olofsson, K Erik J and Park, Seolhye and Peterson, J Luc and
Probst, Michael and Pugmire, Dave and Sammuli, Brian and
Sawlani, Kapil and Scheinker, Alexander and Schissel, David P
and Shalloo, Rob J and Shinagawa, Jun and Seong, Jaegu and
Spears, Brian K and Tennyson, Jonathan and Thiagarajan,
Jayaraman and Tico{\c s}, Catalin M and Trieschmann, Jan and van
Dijk, Jan and Van Essen, Brian and Ventzek, Peter and Wang,
Haimin and Wang, Jason T L and Wang, Zhehui and Wende, Kristian
and Xu, Xueqiao and Yamada, Hiroshi and Yokoyama, Tatsuya and
Zhang, Xinhua",
abstract = "Data science and technology offer transformative tools and
methods to science. This review article highlights latest
development and progress in the interdisciplinary field of
data-driven plasma science (DDPS). A large amount of data and
machine learning algorithms go hand in hand. Most plasma data,
whether experimental, observational or computational, are
generated or collected by machines today. It is now becoming
impractical for humans to analyze all the data manually.
Therefore, it is imperative to train machines to analyze and
interpret (eventually) such data as intelligently as humans but
far more efficiently in quantity. Despite the recent impressive
progress in applications of data science to plasma science and
technology, the emerging field of DDPS is still in its infancy.
Fueled by some of the most challenging problems such as fusion
energy, plasma processing of materials, and fundamental
understanding of the universe through observable plasma
phenomena, it is expected that DDPS continues to benefit
significantly from the interdisciplinary marriage between plasma
science and data science into the foreseeable future.",
publisher = "arXiv",
year = 2022
}
@article{Boehnlein2022,
doi = {10.1103/revmodphys.94.031003},
url = {https://doi.org/10.1103/revmodphys.94.031003},
year = {2022},
month = sep,
publisher = {American Physical Society ({APS})},
volume = {94},
number = {3},
author = {Amber Boehnlein and Markus Diefenthaler and Nobuo Sato and Malachi Schram and Veronique Ziegler and Cristiano Fanelli and Morten Hjorth-Jensen and Tanja Horn and Michelle P. Kuchera and Dean Lee and Witold Nazarewicz and Peter Ostroumov and Kostas Orginos and Alan Poon and Xin-Nian Wang and Alexander Scheinker and Michael S. Smith and Long-Gang Pang},
title = {$\less$i$\greater$Colloquium$\less$/i$\greater$
: Machine learning in nuclear physics},
journal = {Reviews of Modern Physics}
}
@ARTICLE{Hatfield2021-cl,
title = "The data-driven future of high-energy-density physics",
author = "Hatfield, Peter W and Gaffney, Jim A and Anderson, Gemma J and
Ali, Suzanne and Antonelli, Luca and Ba{\c s}e{\u g}mez du Pree,
Suzan and Citrin, Jonathan and Fajardo, Marta and Knapp, Patrick
and Kettle, Brendan and Kustowski, Bogdan and MacDonald, Michael
J and Mariscal, Derek and Martin, Madison E and Nagayama,
Taisuke and Palmer, Charlotte A J and Peterson, J Luc and Rose,
Steven and Ruby, J J and Shneider, Carl and Streeter, Matt J V
and Trickey, Will and Williams, Ben",
abstract = "High-energy-density physics is the field of physics concerned
with studying matter at extremely high temperatures and
densities. Such conditions produce highly nonlinear plasmas, in
which several phenomena that can normally be treated
independently of one another become strongly coupled. The study
of these plasmas is important for our understanding of
astrophysics, nuclear fusion and fundamental physics-however,
the nonlinearities and strong couplings present in these extreme
physical systems makes them very difficult to understand
theoretically or to optimize experimentally. Here we argue that
machine learning models and data-driven methods are in the
process of reshaping our exploration of these extreme systems
that have hitherto proved far too nonlinear for human
researchers. From a fundamental perspective, our understanding
can be improved by the way in which machine learning models can
rapidly discover complex interactions in large datasets. From a
practical point of view, the newest generation of extreme
physics facilities can perform experiments multiple times a
second (as opposed to approximately daily), thus moving away
from human-based control towards automatic control based on
real-time interpretation of diagnostic data and updates of the
physics model. To make the most of these emerging opportunities,
we suggest proposals for the community in terms of research
design, training, best practice and support for synthetic
diagnostics and data analysis.",
journal = "Nature",
publisher = "Springer Science and Business Media LLC",
volume = 593,
number = 7859,
pages = "351--361",
month = may,
year = 2021,
language = "en"
}